This quarter · released September 30

PyTorch 2.14.1 fixes an NVFP4 bug; scope isn't quantified

PyTorch 2.14.1 moves to CUDA 13.2.2 and fixes a cuBLAS bug that could ignore the tensor-wide scaling factor in NVFP4 matrix multiplications. The release notes say: "cuBLAS: `cublasLtMatmul()` could ignore tensor-wide scaling for NVFP4 matrix multiplications (introduced in CUDA 13.2 Update 1)" (PyTorch 2.14.1 release notes, 2026-09-30). NVFP4 is the 4-bit floating-point format used on NVIDIA Blackwell. On September 2, version 2.14.0 of the same series pointed the Inductor compiler at the next-generation Rubin architecture (sm_107) and built in NVFP4 matrix multiplication (PyTorch 2.14.0 release notes, 2026-09-02). The release notes don't quantify the scope of the bug or give failure cases. How we checked →

Concept illustration: a black-framed computer monitor on a stand rests on a thin horizontal line against an off-white background. Its screen shows two stacked windows: a narrower light-grey window on top, with three dots, three grey lines and a white box lettered "PYTORCH 2.14.1", and directly beneath it a full-width brick-red window with three dots, three pale lines and a white box lettered "CUDA 13.2.2".

Source and checks

This item ran in the brief of Oct 16, 2026, alongside 19 other items. How we checked it, the quote checks and what was still missing that day are on that issue's How we checked page.

Cite this item

SecondSource (2026). "PyTorch 2.14.1 fixes an NVFP4 bug; scope isn't quantified." https://secondsource.io/en/news/2026-10-16-8/. Accessed access date.